Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@GLM OCR MCP Serverextract the text from document.pdf"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
GLM OCR MCP Server
MCP server for extracting text from images and PDFs using ZhipuAI GLM-OCR.
Installation
Install from source (recommended)
# Clone the repository
git clone https://github.com/yourusername/glm-ocr-mcp.git
cd glm-ocr-mcp
uvx --from . glm-ocr-mcp
Run directly
# Run with uvx (requires setting environment variable)
ZHIPU_API_KEY=your_api_key uvx --from glm-ocr-mcp glm-ocr-mcp
Configuration
Set the environment variable:
export ZHIPU_API_KEY=your_api_key_here
Usage
Using with Claude Code
Add to ~/.claude/mcp.json:
{
"mcpServers": {
"glm-ocr": {
"command": "uvx",
"args": ["--from", "glm-ocr-mcp", "glm-ocr-mcp"],
"env": {
"ZHIPU_API_KEY": "your_api_key_here"
}
}
}
}
Tools
The server provides extract_text tool:
file_path: Path to image or PDF file (absolute or relative to working directory)
base64_data: Base64 encoded file data (optional, either file_path or base64_data required)
Examples
# Extract text from image
extract_text(file_path="./screenshot.png")
# Extract text from PDF
extract_text(file_path="./document.pdf")
# Use base64 data
extract_text(base64_data="data:image/png;base64,iVBORw0KGgo...")
Development
# Create virtual environment
uv venv
source .venv/bin/activate
# Install dependencies
uv pip install -e .
# Run server for testing
python -m glm_ocr_mcp.server
Project Structure
glm-ocr-mcp/
├── pyproject.toml # Project configuration
├── README.md # Documentation
├── .env.example # Environment variable template
├── src/
│ └── glm_ocr_mcp/
│ ├── __init__.py
│ ├── __main__.py # Entry point
│ ├── ocr.py # OCR client
│ └── server.py # MCP server